Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead Optimization.

Early evaluation of absorption, distribution, metabolism, and excretion (ADME) properties is crucial for streamlining drug development. Traditional in vivo and in vitro approaches are often expensive. Moreover, during lead optimization, these methods rely heavily on the expertise of specialists, leading to efficiency challenges. Consequently, in silico methods for ADME prediction are attracting increasing attention. However, existing in silico methods face two major issues: a decline in predictive performance caused by limited ADME data and a lack of clarity regarding the rationale for lead optimization to improve ADME properties. In this study, we built an AI model capable of predicting ten different ADME parameters to overcome these challenges. Our training approach was based on a graph neural network combining multitask learning, which shares information across multiple tasks to increase the number of usable samples, with fine-tuning that adapts to each task. In addition, we applied the integrated gradients method to compound data collected before and after lead optimization to quantify and interpret each input feature's contribution to the predicted ADME values. Our proposed model achieved the highest performance for seven of the ten ADME parameters compared with conventional methods. Furthermore, visualization of the changes in chemical structures before and after lead optimization revealed that the model's explanations aligned well with established chemical insights. These results suggest that data-driven approaches may assist molecular design by providing complementary insights into empirical rules.